User Activity Classification and Domain-Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

User’s Activity Classification And Domain Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Non-Volatile Organic Compounds in Exhaled Breath Particles Correspond to Active Tuberculosis

Human breath contains trace amounts of non-volatile organic compounds (NOCs) which might inform non-invasive methods for evaluation of individual health. In previous work, we demonstrated that lipids detected in exhaled breath aerosol (EBA) could be used as markers of active tuberculosis (TB). Here, we advanced our analytical platform in characterizing small metabolites and lipids in EBA samples collected from participants enrolled in clinical trials designed to identify molecular signatures of active TB. EBA samples from 26 participants with active TB and 73 healthy participants were processed using a dual-phase extraction method, and metabolites and lipids were identified via mass spectrometry (MS) database matching. In total, 13 metabolite and 9 lipid markers were identified with optimized relative standard deviation values that were statistically different between individuals diagnosed with active TB and the healthy controls. A feature ranking algorithm reduced this number to 10 molecules, with the membrane glycerophospholipid, phosphatidylinositol 24:4, emerging as top driver of segregation between the two groups. These results support the utility of this approach to identify consistent NOC signatures from EBA samples in active TB cases and suggest the potential to apply this method to other human diseases which alter respiratory NOC release.

Logics, tensions and negotiations in the everyday life of a news-ranking algorithm

This article attends to tensions and negotiations surrounding the introduction and development of a news-ranking algorithm in a Swedish daily. Approaching algorithms as culture, being composed of collective human practices, the study emphasizes socio-institutional dynamics in the everyday life of the algorithm. The focus on tensions and negotiations is justified from an institutional perspective and operationalized through an analytical framework of logics. Empirically the study is based on interviews with 14 different in-house workers at the daily, journalists as well as programmers and market actors. The study shows that logics connected to both journalism and programming co-developed the news-ranking algorithm. Tensions and their negotiations around these logics contributed to its very development. One example is labeling of the algorithm as editor-led, allowing journalists to oversee some of its parameters. Social practices in the newsroom, such as Algorithm-Coffee, was also important for its development. In other words, different actors, tensions between them and how these were negotiated, co-constituted by the algorithm itself.

Improving Data Augmentation for Low-Resource NMT Guided by POS-Tagging and Paraphrase Embedding

Data augmentation is an approach for several text generation tasks. Generally, in the machine translation paradigm, mainly in low-resource language scenarios, many data augmentation methods have been proposed. The most used approaches for generating pseudo data mainly lay in word omission, random sampling, or replacing some words in the text. However, previous methods barely guarantee the quality of augmented data. In this work, we try to build the data by using paraphrase embedding and POS-Tagging. Namely, we generate the fake monolingual corpus by replacing the main four POS-Tagging labels, such as noun, adjective, adverb, and verb, based on both the paraphrase table and their similarity. We select the bigger corpus size of the paraphrase table with word level and obtain the word embedding of each word in the table, then calculate the cosine similarity between these words and tagged words in the original sequence. In addition, we exploit the ranking algorithm to choose highly similar words to reduce semantic errors and leverage the POS-Tagging replacement to mitigate syntactic error to some extent. Experimental results show that our augmentation method consistently outperforms all previous SOTA methods on the low-resource language pairs in seven language pairs from four corpora by 1.16 to 2.39 BLEU points.

Ranking the Return on Assets of Tehran Stock Exchange by New Method Based on Z-numbers Data

Since real-world data is often inaccurate and working with fuzzy data and Z-numbers are very important and necessary, in the real world we need to rank and compare data. In this paper, we introduce a new method for ranking Z-numbers. This ranking algorithm is based on centroid point.We evaluate distance between centroid point, and based on this distance, we rank the Z-numbers.We use this method in two practical examples. First in ranking the return on assets of Tehran stock exchange, and second, in ranking of factors affecting the productivity of tourism security.The advantage of this method over conventional fuzzy methods is considering uncertainty, and allocating credit in the opinion of experts to estimate fuzzy parameters.

Identifying Influential Edges by Node Influence Distribution and Dissimilarity Strategy

Identifying influential edges in a complex network is a fundamental topic with a variety of applications. Considering the topological structure of networks, we propose an edge ranking algorithm DID (Dissimilarity Influence Distribution), which is based on node influence distribution and dissimilarity strategy. The effectiveness of the proposed method is evaluated by the network robustness R and the dynamic size of the giant component and compared with well-known existing metrics such as Edge Betweenness index, Degree Product index, Diffusion Intensity and Topological Overlap index in nine real networks and twelve BA networks. Experimental results show the superiority of DID in identifying influential edges. In addition, it is verified through experimental results that the effectiveness of Degree Product and Diffusion Intensity algorithm combined with node dissimilarity strategy has been effectively improved.

Melanoma Detection by Meta-Heuristically-Optimized MLP Parameters Using Non-Dermatoscopy Images

This paper represents a scheme of melanoma detection using handcrafted feature set with meta-heuristically optimized multilayer perceptron (MLP) parameters. Features including shape, color, and texture are extracted from camera images of skin lesion collected from University of Waterloo database. The features are used in two different ways for binary classification of the data into benign and malignant class. 1) The extracted features are ranked on their relevance using ReleifF ranking algorithm and also converted into PCA components and ranked according to their variance. Best result is obtained with 50 best ranked raw features with accuracy of 87.1%. 2) All 1,888 features are fed to an MLP with two hidden layers, with number of neurons optimized by two different metaheuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA) separately. The latter method is found to be more efficient, and an accuracy of 88.38%, sensitivity of 92.22%, and specificity of 83.07% are achieved by PSO, which is better in comparison with the latest research on this dataset.

Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care

Objectives To develop an online crowdsourcing platform where oncologists and other survivorship experts can adjudicate risk for complications in follow-up. Materials and Methods This platform, called Follow-up Interactive Long-Term Expert Ranking (FILTER), prompts participants to adjudicate risk between each of a series of pairs of synthetic cases. The Elo ranking algorithm is used to assign relative risk to each synthetic case. Results The FILTER application is currently live and implemented as a web application deployed on the cloud. Discussion While guidelines for following cancer survivors exist, refinement of survivorship care based on risk for complications after active treatment could improve both allocation of resources and individual outcomes in long-term follow-up. Conclusion FILTER provides a means for a large number of experts to adjudicate risk for survivorship complications with a low barrier of entry.